{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "de864946-1bb2-4843-8d04-a3e61f0438aa",
   "metadata": {},
   "source": [
    "# Env"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5465db86",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set base path\n",
    "BASE_PATH = \"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0237e9ff-5b30-45fd-8dc8-b69b577d5784",
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "from pyspark.sql import SparkSession\n",
    "from pyspark.sql.functions import broadcast, col, countDistinct, row_number, floor\n",
    "from pyspark.sql.window import Window\n",
    "import os\n",
    "\n",
    "# Check if there is an active Spark session\n",
    "spark= SparkSession.getActiveSession()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5b7e50b5-a79c-4d48-80e8-aa67a38756f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize large spark session\n",
    "#\n",
    "# spark.stop()\n",
    "# spark = (\n",
    "#         SparkSession.builder\n",
    "#         .appName(\"GBIF EDA\")\n",
    "#         .config(\"spark.executor.instances\", \"80\")\n",
    "#         .config(\"spark.executor.memory\", \"75G\")\n",
    "#         .config(\"spark.executor.cores\", \"12\")\n",
    "#         .config(\"spark.driver.memory\", \"64G\")\n",
    "#         # Additional Tunning\n",
    "#         .config(\"spark.sql.shuffle.partitions\", \"1000\")\n",
    "#         #.config(\"spark.sql.files.maxPartitionBytes\", \"256MB\")\n",
    "#         .config(\"spark.sql.parquet.enableVectorizedReader\", \"false\") \n",
    "#         .config(\"spark.sql.parquet.compression.codec\", \"snappy\")\n",
    "#         .config(\"spark.serializer\", \"org.apache.spark.serializer.KryoSerializer\")\n",
    "#         .getOrCreate()\n",
    "#     )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "44a3b11e-f7dd-4b0d-8342-357dbad6432d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "sys.path.append(os.path.abspath(\"..\"))\n",
    "\n",
    "from pyspark.sql import functions as F\n",
    "from pyspark.sql import types as T\n",
    "from pyspark.sql.functions import when, col, countDistinct, count, desc, broadcast, lower\n",
    "\n",
    "from helpers.variables import COLS_TAXONOMIC\n",
    "from helpers.data_analysis import init_spark, create_freq, view_freq, check_sparsity\n",
    "from helpers.gbif import fetch_gbif_chunk, fetch_gbif_iter, retry_failed_chunks, insert_records_to_mongo, fetch_publisher_key\n",
    "#from helpers.text_search import flatten_dict, full_text_search_rdd, flatten_list_to_string, extract_fields"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "368e3681-3401-4b46-83f1-5d27316f47e7",
   "metadata": {},
   "source": [
    "# Identify Museum Specimen Images"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "212a7f11-39f5-46c4-b0d2-2f935443892b",
   "metadata": {},
   "source": [
    "We use `basisOfRecord` to identify museum specimen images:\n",
    "\n",
    "identify occurrences in the following categories of museum specimen images:\n",
    "- Preserved specimens\n",
    "- Fossil specimens\n",
    "- Material samples\n",
    "- Living specimens\n",
    "- Material citations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fe9a98a-0a67-4e78-bbca-9d09a808ea49",
   "metadata": {},
   "source": [
    "Create a lookup table that contains the file paths. Partition this lookup table into groups such that each group's unique file count is not greater than 100. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "743263af-e326-4118-a4b0-a4f7f3537a20",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_lookup_tbl(spark):\n",
    "\n",
    "    COLS_TAXONOMIC = [\n",
    "        \"kingdom\", \"phylum\", \"class\", \"order\", \"family\", \"genus\", \"species\"\n",
    "    ]\n",
    "    N_MAX_FILES = 100  # Maximum files per group\n",
    "\n",
    "    spark_df = spark.read.parquet(f\"{BASE_PATH}/gbif/attributes/cols_of_interest\")\n",
    "    lookup_tbl = spark.read.parquet(f\"{BASE_PATH}/gbif/lookup_tables/2024-05-01/lookup_tables\")\n",
    "\n",
    "    # Identify source IDs with multiple UUIDs\n",
    "    source_id_with_multiple_uuids = (\n",
    "        spark_df.groupBy(\"source_id\")\n",
    "        .agg(countDistinct(\"uuid\").alias(\"distinct_uuid_count\"))\n",
    "        .filter(col(\"distinct_uuid_count\") > 1)\n",
    "    )\n",
    "\n",
    "    # Filter museum specimen records and join with source IDs and lookup table\n",
    "    lookup_tbl_museum_specimen = (\n",
    "        spark_df\n",
    "        .filter(\n",
    "            col(\"basisOfRecord\").isin(\n",
    "                [\n",
    "                    \"PRESERVED_SPECIMEN\", \"MATERIAL_SAMPLE\", \"FOSSIL_SPECIMEN\",\n",
    "                    \"LIVING_SPECIMEN\", \"MATERIAL_CITATION\"\n",
    "                ]\n",
    "            )\n",
    "        )\n",
    "        .join(source_id_with_multiple_uuids, on=\"source_id\", how=\"inner\")\n",
    "        .select([\"uuid\", \"source_id\"] + COLS_TAXONOMIC)\n",
    "        .join(lookup_tbl, on=\"uuid\", how=\"inner\")\n",
    "    )\n",
    "\n",
    "    # Group paths into batches of N_MAX_FILES\n",
    "    unique_paths = lookup_tbl_museum_specimen.select(\"path\").distinct()\n",
    "    window_spec = Window.orderBy(\"path\")\n",
    "\n",
    "    unique_paths_with_row = unique_paths.withColumn(\n",
    "        \"row_number\", row_number().over(window_spec)\n",
    "    )\n",
    "\n",
    "    grouped_paths = unique_paths_with_row.withColumn(\n",
    "        \"group_id\", floor((col(\"row_number\") - 1) / N_MAX_FILES)\n",
    "    ).drop(\"row_number\")\n",
    "\n",
    "    # Join grouped paths with the main table\n",
    "    result_lookup_tbl = lookup_tbl_museum_specimen.join(\n",
    "        grouped_paths, on=\"path\", how=\"left\"\n",
    "    )\n",
    "\n",
    "    # Write the result to a partitioned Parquet file\n",
    "    result_lookup_tbl.repartition(1, \"group_id\").write.partitionBy(\"group_id\").mode(\n",
    "        \"overwrite\"\n",
    "    ).parquet(\n",
    "        f\"{BASE_PATH}/gbif/lookup_tables/2024-05-01/lookup_multi_images_museum_specimen\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "44c3769d-c592-4172-8d5a-b6e960bbc114",
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_group(spark, base_input_path, base_output_path, group_id):\n",
    "\n",
    "    # Construct paths for the current group\n",
    "    group_input_path = f\"{base_input_path}/group_id={group_id}\"\n",
    "    group_output_path = f\"{base_output_path}/group_id={group_id}\"\n",
    "\n",
    "\n",
    "    filtered_df = spark.read.parquet(group_input_path)\n",
    "    unique_paths = [row['path'] for row in filtered_df.select(\"path\").distinct().collect()]\n",
    "    \n",
    "    # Read the combined DataFrame from unique paths\n",
    "    combined_df = spark.read.parquet(*unique_paths).select([\"uuid\", \"original_size\", \"resized_size\", \"image\"])\n",
    "\n",
    "    result_df = combined_df.join(broadcast(filtered_df), on=\"uuid\", how=\"inner\")\n",
    "    result_df = result_df.dropDuplicates([\"uuid\"]).repartition(100)\n",
    "    \n",
    "    # Write the result to the output path\n",
    "    result_df.write.mode(\"overwrite\").parquet(group_output_path)\n",
    "    print(f\"Processed and saved results for group_id={group_id} to {group_output_path}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "332cb967-6923-469e-90e1-32261c97a8e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "base_input_path = f\"{BASE_PATH}/gbif/lookup_tables/2024-05-01/lookup_multi_images_museum_specimen\"\n",
    "base_output_path = f\"{BASE_PATH}/gbif/image_lookup/multi_images_museum_specimen\"\n",
    "\n",
    "group_folders = sorted(\n",
    "    [int(f.split(\"=\")[-1]) for f in os.listdir(base_input_path) if f.startswith(\"group_id=\")]\n",
    ")\n",
    "\n",
    "# Process each group iteratively\n",
    "for group_id in group_folders:\n",
    "    process_group(spark, base_input_path, base_output_path, str(group_id))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbd602b3-68c8-4320-9436-baf37b381206",
   "metadata": {},
   "source": [
    "# Add Category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ad4b2dc2-a028-44c4-a439-16c0b6ac66ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "spark_df = spark.read.parquet(f\"{BASE_PATH}/gbif/attributes/cols_of_interest\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed5ebb68-4f16-4b37-801c-1ea2df3ad499",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "lookup_tbl_museum_specimen = spark.read.parquet(f\"{BASE_PATH}/gbif/lookup_tables/2024-05-01/lookup_multi_images_museum_specimen\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1b3005be-fd31-44da-96e5-315aa51be5ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- path: string (nullable = true)\n",
      " |-- uuid: string (nullable = true)\n",
      " |-- source_id: string (nullable = true)\n",
      " |-- basisOfRecord: string (nullable = true)\n",
      " |-- kingdom: string (nullable = true)\n",
      " |-- phylum: string (nullable = true)\n",
      " |-- class: string (nullable = true)\n",
      " |-- order: string (nullable = true)\n",
      " |-- family: string (nullable = true)\n",
      " |-- genus: string (nullable = true)\n",
      " |-- species: string (nullable = true)\n",
      " |-- group_id: integer (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "lookup_tbl_museum_specimen.printSchema()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1936bedd-4930-492f-bae5-bf1b93e271fb",
   "metadata": {},
   "source": [
    "Total number of images:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1f24cbe2-8cf1-45c9-964d-2442a118c67e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "data": {
      "text/plain": [
       "9491690"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lookup_tbl_museum_specimen.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3331dd5-13bf-4d8d-a6d8-343a7ecb5a7e",
   "metadata": {},
   "source": [
    "Total number of occurrence:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9be1abf3-cbcc-4a03-8229-4efb3370150a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "data": {
      "text/plain": [
       "3698000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lookup_tbl_museum_specimen.select(\"source_id\").distinct().count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7b3678b7-9633-49d8-9d39-3d98fdc4a8ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_df = (\n",
    "    spark_df\n",
    "    .join(\n",
    "        lookup_tbl_museum_specimen.select(\"uuid\"),\n",
    "        on = \"uuid\",\n",
    "        how = \"inner\"\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5fb8e907-7669-4c4f-bdbf-fd060a188d30",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "from pyspark.sql.functions import col, when, mean, sum as spark_sum\n",
    "\n",
    "# Generate boolean columns and cast to integers for aggregation\n",
    "spark_df_with_flags = filtered_df.withColumn(\n",
    "    \"is_taxon_higher_rank\", \n",
    "    when(col(\"issue\").contains(\"TAXON_MATCH_HIGHERRANK\"), 1).otherwise(0)\n",
    ").withColumn(\n",
    "    \"is_taxon_match_none\", \n",
    "    when(col(\"issue\").contains(\"TAXON_MATCH_NONE\"), 1).otherwise(0)\n",
    ").withColumn(\n",
    "    \"is_taxon_match_fuzzy\", \n",
    "    when(col(\"issue\").contains(\"TAXON_MATCH_FUZZY\"), 1).otherwise(0)\n",
    ").withColumn(\n",
    "    \"is_any\", \n",
    "    when((col(\"issue\").contains(\"TAXON_MATCH_FUZZY\")) | (col(\"issue\").contains(\"TAXON_MATCH_NONE\")) | (col(\"issue\").contains(\"TAXON_MATCH_HIGHERRANK\")), 1).otherwise(0)\n",
    ")\n",
    "\n",
    "# Calculate sum and mean for each numeric column\n",
    "result_df = spark_df_with_flags.agg(\n",
    "    spark_sum(\"is_taxon_higher_rank\").alias(\"sum_taxon_higher_rank\"),\n",
    "    mean(\"is_taxon_higher_rank\").alias(\"mean_taxon_higher_rank\"),\n",
    "    spark_sum(\"is_taxon_match_none\").alias(\"sum_taxon_match_none\"),\n",
    "    mean(\"is_taxon_match_none\").alias(\"mean_taxon_match_none\"),\n",
    "    spark_sum(\"is_taxon_match_fuzzy\").alias(\"sum_taxon_match_fuzzy\"),\n",
    "    mean(\"is_taxon_match_fuzzy\").alias(\"mean_taxon_match_fuzzy\"),\n",
    "    spark_sum(\"is_any\").alias(\"sum_is_any\"),\n",
    "    mean(\"is_any\").alias(\"mean_is_any\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c453fa19-ce1f-4cb3-ac83-cb4ceb98a83c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------------------+----------------------+--------------------+---------------------+---------------------+----------------------+----------+-------------------+\n",
      "|sum_taxon_higher_rank|mean_taxon_higher_rank|sum_taxon_match_none|mean_taxon_match_none|sum_taxon_match_fuzzy|mean_taxon_match_fuzzy|sum_is_any|mean_is_any        |\n",
      "+---------------------+----------------------+--------------------+---------------------+---------------------+----------------------+----------+-------------------+\n",
      "|715078               |0.07533726870557297   |256979              |0.027074103768665012 |142789               |0.015043580226492858  |1114846   |0.11745495270073085|\n",
      "+---------------------+----------------------+--------------------+---------------------+---------------------+----------------------+----------+-------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "result_df.show(truncate=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "74038bd1-e18a-43e4-a0c0-8a976c4566e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------------+-------+---------+\n",
      "|basisOfRecord     |count  |bucket   |\n",
      "+------------------+-------+---------+\n",
      "|PRESERVED_SPECIMEN|8326477|5m-10m   |\n",
      "|FOSSIL_SPECIMEN   |638273 |500k-1m  |\n",
      "|MATERIAL_SAMPLE   |448903 |100k-500k|\n",
      "|LIVING_SPECIMEN   |76642  |50k-100k |\n",
      "|MATERIAL_CITATION |1395   |1k-5k    |\n",
      "+------------------+-------+---------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "view_freq(lookup_tbl_museum_specimen, \"basisOfRecord\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f07fd54-2a84-4601-9789-611042f1132f",
   "metadata": {},
   "source": [
    "\n",
    "|**Category**|**Description**|\n",
    "|---|---|\n",
    "|**Plant**|Includes Plantae and related phyla like Magnoliopsida and Ochrophyta (e.g., seaweed).|\n",
    "|**Fungi**|Includes Fungi kingdom and related phyla like Mycetozoa, Oomycota, Leotiomycetes, and Sordariomycetes.|\n",
    "|**Insect**|Includes all organisms in the `class = \"Insecta\"`.|\n",
    "|**Invertebrate Zoology**|Marine and terrestrial invertebrates across multiple phyla, excluding Insecta.|\n",
    "|**Vertebrate Zoology - Amphibians**|Organisms in `class = \"Amphibia\"`.|\n",
    "|**Vertebrate Zoology - Reptiles**|Organisms in `class = \"Reptilia\"`.|\n",
    "|**Vertebrate Zoology - Birds**|Organisms in `class = \"Aves\"`.|\n",
    "|**Vertebrate Zoology - Fishes**|Organisms in `phylum = \"Chordata\"` and in various fish-related orders|\n",
    "|**Vertebrate Zoology - Mammals**|Organisms in `class = \"Mammalia\"`.|\n",
    "|**Vertebrate Zoology - Others**|Remaining Chordata organisms not in other vertebrate subcategories.|\n",
    "|**Microbiology**|Includes unicellular organisms and protists in microbiologically relevant phyla.|\n",
    "|**Uncategorized**|Records that do not meet any defined conditions, incertae sedis, miscellaneous, misalignment, etc.|\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "30d1fbfb-129b-4e00-a3a5-91ae5829630c",
   "metadata": {},
   "outputs": [],
   "source": [
    "fish_classes = [\n",
    "    \"Agnatha\",                    # Jawless fish\n",
    "    \"Myxini\",                     # Hagfish\n",
    "    \"Pteraspidomorphi\",           # Early jawless fish (extinct)\n",
    "    \"Thelodonti\",                 # Extinct\n",
    "    \"Anaspida\",                   # Extinct\n",
    "    \"Petromyzontida\",             # Lampreys\n",
    "    \"Hyperoartia\",\n",
    "    \"Conodonta\",                  # Conodonts (extinct)\n",
    "    \"Cephalaspidomorphi\",         # Early jawless fish (extinct)\n",
    "    \"Placodermi\",                 # Armoured fish (extinct)\n",
    "    \"Acanthodii\",                 # Spiny sharks (extinct)\n",
    "    \"Actinopterygii\",             # Ray-finned fish\n",
    "    \"Sarcopterygii\"               # Lobe-finned fish\n",
    "    \"Chondrichthyes\",             # cartilaginous fish\n",
    "    \"Sarcopterygii\"              # lobe-finned fish\n",
    "]\n",
    "\n",
    "fish_orders = [\n",
    "    # Class Myxini\n",
    "    \"Myxiniformes\",\n",
    "    # Class Cephalaspidomorphi\n",
    "    \"Petromyzontiformes\",\n",
    "    \n",
    "    # Class Chondrichthyes (Cartilaginous Fishes)\n",
    "    \"Selachii\",          # Sharks\n",
    "    \"Batoidei\",          # Rays, sawfishes, guitarfishes, skates, stingrays\n",
    "    \"Chimaeriformes\",    # Chimaeras\n",
    "\n",
    "    # Class Actinopterygii (Ray-Finned Fishes)\n",
    "    # Subclass Chondrostei\n",
    "    \"Acipenseriformes\",  # Sturgeons and paddlefishes\n",
    "    \"Polypteriformes\",   # Bichirs and reedfish\n",
    "\n",
    "    # Infraclass Holostei\n",
    "    \"Amiiformes\",        # Bowfins\n",
    "    \"Semionotiformes\",   # Gars\n",
    "\n",
    "    # Infraclass Teleostei (Advanced Bony Fishes)\n",
    "    # Superorder Osteoglossomorpha\n",
    "    \"Osteoglossiformes\", # Bonytongues, mooneyes, knife fishes, mormyrs\n",
    "\n",
    "    # Superorder Elopomorpha\n",
    "    \"Elopiformes\",       # Ladyfishes and tarpons\n",
    "    \"Albuliformes\",      # Bonefishes\n",
    "    \"Anguilliformes\",    # eels\n",
    "    \"Saccopharyngiformes\",# gulpers\n",
    "\n",
    "    # Superorder Clupeomorpha\n",
    "    \"Clupeiformes\",      # Herrings and anchovies\n",
    "\n",
    "    # Superorder Ostariophysi\n",
    "    \"Gonorynchiformes\",  # Milkfishes\n",
    "    \"Cypriniformes\",     # Carps, minnows, loaches\n",
    "    \"Characiformes\",     # Characins, tetras, piranhas\n",
    "    \"Siluriformes\",      # Catfishes\n",
    "    \"Gymnotiformes\",     # Knifefishes, electric eels\n",
    "\n",
    "    # Superorder Protacanthopterygii\n",
    "    \"Salmoniformes\",     # Salmons, trouts, and allies\n",
    "    \"Esociformes\",       # Pikes and pickerels\n",
    "    \"Osmeriformes\",      # Argentines and smelts\n",
    "\n",
    "    # Superorder Paracanthopterygii\n",
    "    \"Percopsiformes\",    # Trout-perches, pirate perches, cave fishes\n",
    "    \"Gadiformes\",        # Cods and allies\n",
    "    \"Lophiiformes\",      # Anglerfishes\n",
    "\n",
    "    \"Stomiiformes\",\n",
    "    \"Ateleopodiformes\",\n",
    "    \"Aulopiformes\",\n",
    "    \"Myctophiformes\",\n",
    "    \"Lampriformes\",\n",
    "    \"Polymixiiformes\",\n",
    "    \"Percopsiformes\",\n",
    "    \"Gadiformes\",\n",
    "    \"Batrachoidiformes\",\n",
    "    \"Lophiiformes\",\n",
    "    \"Ophidiiformes\",\n",
    "    \"Atheriniformes\",\n",
    "    \"Cyprinodontiformes\",\n",
    "    \"Beloniformes\",\n",
    "    \"Mugiliformes\",\n",
    "    \"Stephanoberyciformes\",\n",
    "    \"Beryciformes\",\n",
    "    \"Zeiformes\",\n",
    "    \"Gasterosteiformes\",\n",
    "    \"Synbranchiformes\",\n",
    "    \"Scorpaeniformes\",\n",
    "    \"Perciformes\",\n",
    "    \"Pleuronectiformes\",\n",
    "    \"Tetraodontiformes\",\n",
    "    \"Coelacanthiformes\",\n",
    "    \"Ceratodontiformes\",\n",
    "    \"Lepidosireniformes\"\n",
    "]\n",
    "\n",
    "invertebrate_phyla = [\n",
    "    \"Mollusca\", \"Arthropoda\", \"Bryozoa\", \"Cnidaria\", \"Brachiopoda\", \"Echinodermata\",\n",
    "    \"Porifera\", \"Foraminifera\", \"Tardigrada\", \"Annelida\", \"Nemertea\", \"Nematoda\",\n",
    "    \"Platyhelminthes\", \"Sipuncula\", \"Rotifera\", \"Chaetognatha\", \"Gastrotricha\",\n",
    "    \"Hemichordata\", \"Onychophora\", \"Xenacoelomorpha\", \"Ctenophora\", \"Priapulida\",\n",
    "    \"Acanthocephala\", \"Phoronida\", \"Entoprocta\", \"Nematomorpha\", \"Loricifera\", \"Kinorhyncha\", \"Cycliophora\"\n",
    "]\n",
    "\n",
    "microbiology_phyla = list(set([\n",
    "    \"Proteobacteria\", \"Cyanobacteria\", \"Actinobacteriota\", \"Firmicutes\", \"Firmicutes_C\",\n",
    "    \"Myzozoa\", \"Cercozoa\", \"Bigyra\", \"Ciliophora\", \"Haptophyta\", \"Metamonada\",\n",
    "    \"Euglenozoa\", \"Sarcomastigophora\", \"Bacteroidota\", \"Amoebozoa\", \"Microsporidia\",\n",
    "    \"Cryptophyta\", \"Myxococcota\", \"Fusobacteriota\"\n",
    "]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9b764160-5cfe-4d12-a946-f5d9eb8be15d",
   "metadata": {},
   "outputs": [],
   "source": [
    "cond_plant = (\n",
    "    ((col(\"kingdom\").isNotNull()) & (col(\"kingdom\") == \"Plantae\")) |\n",
    "    ((col(\"phylum\").isNotNull()) & (col(\"phylum\").isin([\"Magnoliopsida\", \"Ochrophyta\"]))) # sea weed\n",
    ")\n",
    "cond_fungi = (\n",
    "    ((col(\"kingdom\").isNotNull()) & (col(\"kingdom\") == \"Fungi\")) |\n",
    "    ((col(\"phylum\").isNotNull()) & (col(\"phylum\").isin([\"Mycetozoa\", \"Oomycota\", \"Leotiomycetes\", \"Sordariomycetes\"])))\n",
    ")\n",
    "cond_insect = (col(\"class\").isNotNull()) & (col(\"class\") == \"Insecta\")\n",
    "cond_invertebrate = (\n",
    "    (col(\"phylum\").isNotNull()) & \n",
    "    (col(\"phylum\").isin(invertebrate_phyla)) & \n",
    "    ((col(\"class\").isNull()) | (col(\"class\") != \"Insecta\"))\n",
    ")\n",
    "\n",
    "cond_amphibian = (\n",
    "    (col(\"phylum\").isNotNull()) & \n",
    "    (col(\"class\").isNotNull()) &\n",
    "    (col(\"phylum\") == \"Chordata\") &\n",
    "    (col(\"class\") == \"Amphibia\")\n",
    ")\n",
    "cond_reptile = (\n",
    "    (col(\"phylum\").isNotNull()) & \n",
    "    (col(\"class\").isNotNull()) &\n",
    "    (col(\"phylum\") == \"Chordata\") &\n",
    "    (col(\"class\") == \"Reptilia\")\n",
    ")\n",
    "cond_bird = (\n",
    "    (col(\"phylum\").isNotNull()) & \n",
    "    (col(\"class\").isNotNull()) &\n",
    "    (col(\"phylum\") == \"Chordata\") &\n",
    "    (col(\"class\") == \"Aves\")\n",
    ")\n",
    "cond_fish = (\n",
    "    (col(\"phylum\").isNotNull()) & \n",
    "    (col(\"order\").isNotNull()) &\n",
    "    (col(\"phylum\") == \"Chordata\") &\n",
    "    (col(\"order\").isin(fish_orders))\n",
    ")\n",
    "cond_mammal = (\n",
    "    (col(\"phylum\").isNotNull()) & \n",
    "    (col(\"class\").isNotNull()) &\n",
    "    (col(\"phylum\") == \"Chordata\") &\n",
    "    (col(\"class\") == \"Mammalia\")\n",
    ")\n",
    "\n",
    "# cond_paleobiology = (\n",
    "#     (col(\"phylum\").isNotNull()) &\n",
    "#     (col(\"phylum\").isin(\n",
    "#         \"Brachiopoda\", \"Foraminifera\"\n",
    "#     ))\n",
    "# )\n",
    "\n",
    "cond_microbiology = (\n",
    "    (col(\"phylum\").isNotNull()) &\n",
    "    (col(\"phylum\").isin(microbiology_phyla))\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "841f7d80-5fd9-4c5d-8e0c-44d01e546e85",
   "metadata": {},
   "outputs": [],
   "source": [
    "lookup_tbl_categorized = lookup_tbl_museum_specimen.withColumn(\n",
    "    \"category\",\n",
    "    when(cond_plant, \"Plant\")\n",
    "    .when(cond_fungi, \"Fungi\")  # Assuming fungi are classified under paleobiology\n",
    "    .when(cond_insect, \"Insect\")\n",
    "    .when(cond_invertebrate, \"Invertebrate Zoology\")\n",
    "    .when(cond_fish, \"Vertebrate Zoology - Fishes\")\n",
    "    .when(cond_amphibian, \"Vertebrate Zoology - Amphibians\")\n",
    "    .when(cond_reptile, \"Vertebrate Zoology - Reptiles\")\n",
    "    .when(cond_bird, \"Vertebrate Zoology - Birds\")\n",
    "    .when(cond_mammal, \"Vertebrate Zoology - Mammals\")\n",
    "    .when(((col(\"phylum\").isNotNull()) & (col(\"phylum\") == \"Chordata\")), \"Vertebrate Zoology - Others\") \n",
    "    #.when(cond_paleobiology, \"Paleobiology\")\n",
    "    .when(cond_microbiology, \"Microbiology\")\n",
    "    .otherwise(\"Uncategorized\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3ecf5395-5c7f-47bc-a2ba-ca1af4bf4bf9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------------------------+-------+---------+\n",
      "|category                       |count  |bucket   |\n",
      "+-------------------------------+-------+---------+\n",
      "|Insect                         |4206096|1m-5m    |\n",
      "|Plant                          |2601735|1m-5m    |\n",
      "|Invertebrate Zoology           |824877 |500k-1m  |\n",
      "|Fungi                          |823069 |500k-1m  |\n",
      "|Vertebrate Zoology - Birds     |382277 |100k-500k|\n",
      "|Uncategorized                  |279530 |100k-500k|\n",
      "|Vertebrate Zoology - Mammals   |119345 |100k-500k|\n",
      "|Vertebrate Zoology - Fishes    |118171 |100k-500k|\n",
      "|Vertebrate Zoology - Others    |101483 |100k-500k|\n",
      "|Vertebrate Zoology - Amphibians|30205  |10k-50k  |\n",
      "|Microbiology                   |4902   |1k-5k    |\n",
      "+-------------------------------+-------+---------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "view_freq(lookup_tbl_categorized, \"category\", truncate=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "65cea995-d34b-4180-8ca3-d97034a39254",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------------+-----+-------+\n",
      "|basisOfRecord     |count|bucket |\n",
      "+------------------+-----+-------+\n",
      "|FOSSIL_SPECIMEN   |35867|10k-50k|\n",
      "|PRESERVED_SPECIMEN|27297|10k-50k|\n",
      "|MATERIAL_SAMPLE   |2512 |1k-5k  |\n",
      "|LIVING_SPECIMEN   |703  |501-1k |\n",
      "+------------------+-----+-------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "view_freq(\n",
    "    lookup_tbl_categorized.filter(\n",
    "        (col(\"category\")==\"Uncategorized\") &\n",
    "        (col(\"kingdom\")!=\"incertae sedis\")\n",
    "    ),\n",
    "    \"basisOfRecord\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "6ae2e225-d4e8-4e20-bfaf-77468a135921",
   "metadata": {
    "lines_to_next_cell": 2,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------------------+-----+-------+\n",
      "|phylum                  |count|bucket |\n",
      "+------------------------+-----+-------+\n",
      "|NULL                    |20782|10k-50k|\n",
      "|2024-04-27T03:19:49.935Z|41   |11-50  |\n",
      "|2519117                 |3    |1-10   |\n",
      "|35                      |2    |1-10   |\n",
      "|US                      |2    |1-10   |\n",
      "|3169789                 |2    |1-10   |\n",
      "+------------------------+-----+-------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "view_freq(\n",
    "    lookup_tbl_categorized.filter(\n",
    "        (col(\"category\")==\"Uncategorized\") &\n",
    "        (col(\"kingdom\")!=\"incertae sedis\")\n",
    "    ),\n",
    "    \"phylum\",\n",
    "    40\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d19ba2bb-cde4-4504-b885-53a996de2cf8",
   "metadata": {},
   "source": [
    "# Repartition by Category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b7e2931-8488-455c-aa66-b25202b4009d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "result_df = spark.read.parquet(f\"{BASE_PATH}/gbif/image_lookup/multi_images_museum_specimen\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e64662e9-56be-4582-847d-4c0d32c16b19",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_df_categorized = result_df.withColumn(\n",
    "    \"category\",\n",
    "    when(cond_plant, \"Plant\")\n",
    "    .when(cond_fungi, \"Fungi\")  # Assuming fungi are classified under paleobiology\n",
    "    .when(cond_insect, \"Insect\")\n",
    "    .when(cond_invertebrate, \"Invertebrate Zoology\")\n",
    "    .when(cond_fish, \"Vertebrate Zoology - Fishes\")\n",
    "    .when(cond_amphibian, \"Vertebrate Zoology - Amphibians\")\n",
    "    .when(cond_reptile, \"Vertebrate Zoology - Reptiles\")\n",
    "    .when(cond_bird, \"Vertebrate Zoology - Birds\")\n",
    "    .when(cond_mammal, \"Vertebrate Zoology - Mammals\")\n",
    "    .when(((col(\"phylum\").isNotNull()) & (col(\"phylum\") == \"Chordata\")), \"Vertebrate Zoology - Others\") \n",
    "    #.when(cond_paleobiology, \"Paleobiology\")\n",
    "    .when(cond_microbiology, \"Microbiology\")\n",
    "    .otherwise(\"Uncategorized\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4f657c25-afc9-407b-9e76-126ebe395f17",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------------------------+-------+---------+\n",
      "|category                       |count  |bucket   |\n",
      "+-------------------------------+-------+---------+\n",
      "|Insect                         |4206096|1m-5m    |\n",
      "|Plant                          |2601001|1m-5m    |\n",
      "|Invertebrate Zoology           |824852 |500k-1m  |\n",
      "|Fungi                          |823069 |500k-1m  |\n",
      "|Vertebrate Zoology - Birds     |382277 |100k-500k|\n",
      "|Uncategorized                  |279530 |100k-500k|\n",
      "|Vertebrate Zoology - Mammals   |119345 |100k-500k|\n",
      "|Vertebrate Zoology - Fishes    |118171 |100k-500k|\n",
      "|Vertebrate Zoology - Others    |101483 |100k-500k|\n",
      "|Vertebrate Zoology - Amphibians|30205  |10k-50k  |\n",
      "|Microbiology                   |4902   |1k-5k    |\n",
      "+-------------------------------+-------+---------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "view_freq(result_df_categorized, [\"category\"], truncate=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "34e0ee4f-ea87-4c0a-b751-3d062d7a787a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "from pyspark.sql.functions import ceil\n",
    "rows_per_partition = 1500\n",
    "\n",
    "partition_tbl = (\n",
    "    result_df_categorized\n",
    "    .groupBy(\"category\")\n",
    "    .count()\n",
    "    .orderBy(desc(\"count\"))\n",
    "    .withColumn(\"partition_count\", ceil(col(\"count\") / rows_per_partition))\n",
    ")\n",
    "\n",
    "partition_dict = {row[\"category\"]: row[\"partition_count\"] for row in partition_tbl.collect()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e6d2bd0f-f7df-497e-943a-2f9e59468a98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Insect': 2805,\n",
       " 'Plant': 1735,\n",
       " 'Invertebrate Zoology': 550,\n",
       " 'Fungi': 549,\n",
       " 'Vertebrate Zoology - Birds': 255,\n",
       " 'Uncategorized': 187,\n",
       " 'Vertebrate Zoology - Mammals': 80,\n",
       " 'Vertebrate Zoology - Fishes': 79,\n",
       " 'Vertebrate Zoology - Others': 68,\n",
       " 'Vertebrate Zoology - Amphibians': 21,\n",
       " 'Microbiology': 4}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "partition_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32733cf4-693f-4351-b6f9-6c2389344a0e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "\n",
    "category = \"Invertebrate Zoology\"\n",
    "partition_count = partition_dict[category]\n",
    "filter_condition = cond_plant\n",
    "\n",
    "(\n",
    "    result_df\n",
    "    .filter(filter_condition)\n",
    "    .repartition(partition_count)\n",
    "    .write.mode(\"overwrite\")\n",
    "    .parquet(f\"{BASE_PATH}/gbif/image_lookup/multi_images_museum_specimen_partitioned_by_category/category={category}\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9918650d-1bf7-4821-81df-a40c5d353503",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Category: Insect, Partitions: 2805\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Written Insect to disk\n",
      "Category: Plant, Partitions: 1735\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Written Plant to disk\n",
      "Category: Invertebrate Zoology, Partitions: 550\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Written Invertebrate Zoology to disk\n",
      "Category: Fungi, Partitions: 549\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Written Fungi to disk\n",
      "Category: Vertebrate Zoology - Birds, Partitions: 255\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Written Vertebrate Zoology - Birds to disk\n",
      "Category: Uncategorized, Partitions: 187\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Written Uncategorized to disk\n",
      "Category: Vertebrate Zoology - Mammals, Partitions: 80\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Written Vertebrate Zoology - Mammals to disk\n",
      "Category: Vertebrate Zoology - Fishes, Partitions: 79\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Written Vertebrate Zoology - Fishes to disk\n",
      "Category: Vertebrate Zoology - Others, Partitions: 68\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Written Vertebrate Zoology - Others to disk\n",
      "Category: Vertebrate Zoology - Amphibians, Partitions: 21\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Written Vertebrate Zoology - Amphibians to disk\n",
      "Category: Microbiology, Partitions: 4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Stage 52:=============================>                            (2 + 2) / 4]\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Written Microbiology to disk\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "for category, partition_count in partition_dict.items():\n",
    "    print(f\"Category: {category}, Partitions: {partition_count}\")\n",
    "    (\n",
    "        result_df_categorized\n",
    "        .filter(\n",
    "            col(\"category\") == category\n",
    "        )\n",
    "        .drop(\"category\")\n",
    "        .repartition(partition_count)\n",
    "        .write.mode(\"overwrite\")\n",
    "        .parquet(f\"{BASE_PATH}/gbif/image_lookup/multi_images_museum_specimen_partitioned_by_category/category={category}\")\n",
    "    )\n",
    "    print(f\"Written {category} to disk\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d074f63-90d3-4c94-a21b-6385f8ad3f4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "partition_result_df = spark.read.parquet(f\"{BASE_PATH}/gbif/image_lookup/multi_images_museum_specimen_partitioned_by_category\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7b0a2bc5-9e2a-488f-9177-c52e0fb4378a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- uuid: string (nullable = true)\n",
      " |-- original_size: array (nullable = true)\n",
      " |    |-- element: long (containsNull = true)\n",
      " |-- resized_size: array (nullable = true)\n",
      " |    |-- element: long (containsNull = true)\n",
      " |-- image: binary (nullable = true)\n",
      " |-- path: string (nullable = true)\n",
      " |-- source_id: string (nullable = true)\n",
      " |-- basisOfRecord: string (nullable = true)\n",
      " |-- kingdom: string (nullable = true)\n",
      " |-- phylum: string (nullable = true)\n",
      " |-- class: string (nullable = true)\n",
      " |-- order: string (nullable = true)\n",
      " |-- family: string (nullable = true)\n",
      " |-- genus: string (nullable = true)\n",
      " |-- species: string (nullable = true)\n",
      " |-- group_id: integer (nullable = true)\n",
      " |-- category: string (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "partition_result_df.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f318d7d0-f610-42a3-b52c-601099ae5672",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "data": {
      "text/plain": [
       "9490931"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "partition_result_df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66f5eb7b-02d6-4e8f-b0ce-8e95bc8e18b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "data_path_root = f\"{BASE_PATH}/gbif/image_lookup/multi_images_museum_specimen\"\n",
    "\n",
    "museum_specimen_df = spark.read.parquet(data_path_root)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "24b45b67-1af8-46bd-a352-c4a95c6dae25",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------------------------+\n",
      "|category                       |\n",
      "+-------------------------------+\n",
      "|Vertebrate Zoology - Amphibians|\n",
      "|Vertebrate Zoology - Fishes    |\n",
      "|Vertebrate Zoology - Mammals   |\n",
      "|Insect                         |\n",
      "|Vertebrate Zoology - Birds     |\n",
      "|Microbiology                   |\n",
      "|Fungi                          |\n",
      "|Invertebrate Zoology           |\n",
      "|Plant                          |\n",
      "|Vertebrate Zoology - Others    |\n",
      "|Uncategorized                  |\n",
      "+-------------------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "museum_specimen_df.select(\"category\").distinct().show(truncate=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "45b03f93-fb6a-461f-8024-55123887f2aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path_insect = os.path.join(data_path_root, \"category=Insect\")\n",
    "museum_specimen_df_insect = spark.read.parquet(data_path_insect)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8baa7142-6246-4b73-8e8a-d2af84321590",
   "metadata": {},
   "source": [
    "# Coverting Binary to Arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4c2304be-e303-4a73-b28b-cc910f7df17b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- uuid: string (nullable = true)\n",
      " |-- original_size: array (nullable = true)\n",
      " |    |-- element: long (containsNull = true)\n",
      " |-- resized_size: array (nullable = true)\n",
      " |    |-- element: long (containsNull = true)\n",
      " |-- image: binary (nullable = true)\n",
      " |-- path: string (nullable = true)\n",
      " |-- source_id: string (nullable = true)\n",
      " |-- basisOfRecord: string (nullable = true)\n",
      " |-- kingdom: string (nullable = true)\n",
      " |-- phylum: string (nullable = true)\n",
      " |-- class: string (nullable = true)\n",
      " |-- order: string (nullable = true)\n",
      " |-- family: string (nullable = true)\n",
      " |-- genus: string (nullable = true)\n",
      " |-- species: string (nullable = true)\n",
      " |-- group_id: integer (nullable = true)\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DataFrame[uuid: string, original_size: array<bigint>, resized_size: array<bigint>, image: binary, path: string, source_id: string, basisOfRecord: string, kingdom: string, phylum: string, class: string, order: string, family: string, genus: string, species: string, group_id: int]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df = result_df.filter(\n",
    "    col(\"uuid\").isin([\n",
    "        \"5ed0f796-c239-4067-9847-2e991c5e6e16\",\n",
    "        \"bb69c2c9-de88-4817-8240-a2f24ecbc54b\",\n",
    "        \"5892077d-3bfb-42f2-a6b6-96fcf6e74176\",\n",
    "        \"cddf7e35-101a-4000-80e4-25aa34fa4c16\",\n",
    "        \"2171905a-644f-44f4-a2e3-22ebce6d40a6\",\n",
    "        \"53011912-f7f5-4b0c-898e-2d47718697af\",\n",
    "        \"bba811df-6ff3-414a-a4be-bf92c264dd3d\",\n",
    "        \"a857fd6c-9b6b-4c73-8a4f-0546e244d1cc\",\n",
    "        \"017804a4-6e28-4fb0-81ea-b4ee34db86e6\",\n",
    "        \"ffffe1b0-9f32-4342-a699-87e718d85b04\"\n",
    "    ])\n",
    ")\n",
    "test_df.printSchema()\n",
    "test_df.cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b5b7984f-c4df-4d11-855a-e7662c8dffdb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def decode_image(image_bytes, original_size, resized_size):\n",
    "    try:\n",
    "        original_height, original_width = original_size\n",
    "        img_array = np.frombuffer(image_bytes, dtype=np.uint8).reshape((original_height, original_width, 3))\n",
    "    except Exception as e:\n",
    "        resized_height, resized_width = resized_size\n",
    "        img_array = np.frombuffer(image_bytes, dtype=np.uint8).reshape((resized_height, resized_width, 3))\n",
    "    \n",
    "    # Convert BGR to RGB\n",
    "    return img_array[..., ::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3a70afd4-7bc7-49f9-95e9-af7a97962b34",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from pyspark.sql.functions import udf\n",
    "from pyspark.sql.types import ArrayType, FloatType\n",
    "\n",
    "def _decode_image(image_bytes, original_size, resized_size):\n",
    "    \"\"\"\n",
    "    Process the raw image bytes using either original_size or resized_size.\n",
    "    Returns a flattened RGB float32 list.\n",
    "    \"\"\"\n",
    "    if image_bytes is None:\n",
    "        return None\n",
    "    \n",
    "    try:\n",
    "        original_height, original_width = original_size\n",
    "        img_array = np.frombuffer(image_bytes, dtype=np.uint8).reshape(\n",
    "            (original_height, original_width, 3)\n",
    "        )\n",
    "    except Exception:\n",
    "        resized_height, resized_width = resized_size\n",
    "        img_array = np.frombuffer(image_bytes, dtype=np.uint8).reshape(\n",
    "            (resized_height, resized_width, 3)\n",
    "        )\n",
    "    \n",
    "    # Convert BGR to RGB\n",
    "    img_rgb = img_array[..., ::-1]\n",
    "    \n",
    "    # Flatten (ravel) and convert to float32\n",
    "    # If you *don't* need flattening for a subsequent step, you can skip this\n",
    "    flattened = img_rgb.astype(np.float32).ravel()\n",
    "    \n",
    "    # Convert back to a Python list so Spark can handle it\n",
    "    return flattened.tolist()\n",
    "\n",
    "# Now wrap this Python function in a Spark UDF\n",
    "decode_image_udf = udf(_decode_image, ArrayType(FloatType()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "547fbaf5-195d-4871-9d9f-b5863dd31444",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_with_features = test_df.withColumn(\n",
    "    \"features\", \n",
    "    decode_image_udf(col(\"image\"), col(\"original_size\"), col(\"resized_size\"))\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e0581519-d3f4-4dd9-aa4f-9b246c5e7191",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- uuid: string (nullable = true)\n",
      " |-- original_size: array (nullable = true)\n",
      " |    |-- element: long (containsNull = true)\n",
      " |-- resized_size: array (nullable = true)\n",
      " |    |-- element: long (containsNull = true)\n",
      " |-- image: binary (nullable = true)\n",
      " |-- path: string (nullable = true)\n",
      " |-- source_id: string (nullable = true)\n",
      " |-- basisOfRecord: string (nullable = true)\n",
      " |-- kingdom: string (nullable = true)\n",
      " |-- phylum: string (nullable = true)\n",
      " |-- class: string (nullable = true)\n",
      " |-- order: string (nullable = true)\n",
      " |-- family: string (nullable = true)\n",
      " |-- genus: string (nullable = true)\n",
      " |-- species: string (nullable = true)\n",
      " |-- group_id: integer (nullable = true)\n",
      " |-- features: array (nullable = true)\n",
      " |    |-- element: float (containsNull = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df_with_features.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e2d6df2f-9182-41d4-8790-23caef050b61",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "df_with_features.drop(col(\"image\")).write.mode(\"overwrite\").parquet(\"test/test_df_with_features\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "377f4e55-78cc-46b3-a202-5787652067fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def _decode_image(image_bytes, original_size, resized_size, flatten=True):\n",
    "    \"\"\"\n",
    "    Process the raw image bytes using either original_size or resized_size.\n",
    "    Optionally returns a flattened or 3D RGB float32 list.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    image_bytes : bytes\n",
    "        The raw image bytes (presumably in BGR format).\n",
    "    original_size : tuple or list\n",
    "        (height, width)\n",
    "    resized_size : tuple or list\n",
    "        (height, width)\n",
    "    flatten : bool\n",
    "        If True, return a 1D array of floats; if False, return a 3D array of floats.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    Python list (1D or nested) of floats in RGB order.\n",
    "    \"\"\"\n",
    "    if image_bytes is None:\n",
    "        return None\n",
    "\n",
    "    try:\n",
    "        original_height, original_width = original_size\n",
    "        img_array = np.frombuffer(image_bytes, dtype=np.uint8).reshape(\n",
    "            (original_height, original_width, 3)\n",
    "        )\n",
    "    except Exception:\n",
    "        resized_height, resized_width = resized_size\n",
    "        img_array = np.frombuffer(image_bytes, dtype=np.uint8).reshape(\n",
    "            (resized_height, resized_width, 3)\n",
    "        )\n",
    "\n",
    "    # Convert BGR to RGB\n",
    "    img_rgb = img_array[..., ::-1].astype(np.float32)\n",
    "\n",
    "    if flatten:\n",
    "        # Flatten to 1D\n",
    "        return img_rgb.ravel().tolist()\n",
    "    else:\n",
    "        # Keep as 3D. Spark can store this as a nested array-of-arrays.\n",
    "        # Convert the NumPy 3D array to nested Python lists\n",
    "        return img_rgb.tolist()\n",
    "\n",
    "# For a nested 3D array: ArrayType(ArrayType(ArrayType(FloatType())))\n",
    "NestedArray3DType = ArrayType(ArrayType(ArrayType(FloatType())))\n",
    "\n",
    "# 1D version (flattened)\n",
    "decode_image_udf_flatten = udf(\n",
    "    lambda image_bytes, original_size, resized_size:\n",
    "        _decode_image(image_bytes, original_size, resized_size, flatten=True),\n",
    "    ArrayType(FloatType())\n",
    ")\n",
    "\n",
    "# 3D version\n",
    "decode_image_udf_3d = udf(\n",
    "    lambda image_bytes, original_size, resized_size:\n",
    "        _decode_image(image_bytes, original_size, resized_size, flatten=False),\n",
    "    NestedArray3DType\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6af6721b-f7aa-439e-88de-a582713ee096",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- uuid: string (nullable = true)\n",
      " |-- original_size: array (nullable = true)\n",
      " |    |-- element: long (containsNull = true)\n",
      " |-- resized_size: array (nullable = true)\n",
      " |    |-- element: long (containsNull = true)\n",
      " |-- image: binary (nullable = true)\n",
      " |-- path: string (nullable = true)\n",
      " |-- source_id: string (nullable = true)\n",
      " |-- basisOfRecord: string (nullable = true)\n",
      " |-- kingdom: string (nullable = true)\n",
      " |-- phylum: string (nullable = true)\n",
      " |-- class: string (nullable = true)\n",
      " |-- order: string (nullable = true)\n",
      " |-- family: string (nullable = true)\n",
      " |-- genus: string (nullable = true)\n",
      " |-- species: string (nullable = true)\n",
      " |-- group_id: integer (nullable = true)\n",
      " |-- features_3d: array (nullable = true)\n",
      " |    |-- element: array (containsNull = true)\n",
      " |    |    |-- element: array (containsNull = true)\n",
      " |    |    |    |-- element: float (containsNull = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df_with_features_3d = test_df.withColumn(\n",
    "    \"features_3d\",\n",
    "    decode_image_udf_3d(\n",
    "        col(\"image\"),\n",
    "        col(\"original_size\"),\n",
    "        col(\"resized_size\")\n",
    "    )\n",
    ")\n",
    "\n",
    "df_with_features_3d.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "eb65de18-72a5-42bf-a77c-67ed04e3873f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "img_arrary = df_with_features_3d.select(\"features_3d\").limit(1).collect()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "815e09bf-18f3-4438-9e32-2698c040a56b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape: (2, 3, 3)\n",
      "Array:\n",
      " [[[255   0   0]\n",
      "  [  0 255   0]\n",
      "  [  0   0 255]]\n",
      "\n",
      " [[255 255   0]\n",
      "  [255   0 255]\n",
      "  [  0 255 255]]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# A small 2 x 3 RGB image\n",
    "# Each 'pixel' is [R, G, B].\n",
    "img_rgb = np.array([\n",
    "    [\n",
    "        [255,   0,   0],  # pixel (row=0, col=0): bright red\n",
    "        [  0, 255,   0],  # pixel (row=0, col=1): bright green\n",
    "        [  0,   0, 255],  # pixel (row=0, col=2): bright blue\n",
    "    ],\n",
    "    [\n",
    "        [255, 255,   0],  # pixel (row=1, col=0): yellow (red+green)\n",
    "        [255,   0, 255],  # pixel (row=1, col=1): magenta (red+blue)\n",
    "        [  0, 255, 255],  # pixel (row=1, col=2): cyan (green+blue)\n",
    "    ]\n",
    "], dtype=np.uint8)\n",
    "\n",
    "print(\"Shape:\", img_rgb.shape)\n",
    "print(\"Array:\\n\", img_rgb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3ab94039-0a9b-49a5-8afe-2f6db24a224b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgMAAAFeCAYAAAAYIxzjAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAAAF8UlEQVR4nO3cMQ7DIBAAwRDx/y+TLxBLlrF2pr7iiitWFIy11voAAFnfpxcAAJ4lBgAgTgwAQJwYAIA4MQAAcWIAAOLEAADEiQEAiJvbk2PcuAb8b/gui9M4Sg6087WglwEAiBMDABAnBgAgTgwAQJwYAIA4MQAAcWIAAOLEAADEiQEAiBMDABAnBgAgTgwAQJwYAIA4MQAAcWIAAOLEAADEiQEAiBMDABAnBgAgTgwAQJwYAIA4MQAAcWIAAOLEAADEiQEAiBMDABAnBgAgTgwAQJwYAIA4MQAAcWIAAOLEAADEiQEAiBMDABAnBgAgTgwAQJwYAIA4MQAAcWIAAOLEAADEiQEAiBMDABAnBgAgTgwAQJwYAIA4MQAAcWIAAOLEAADEiQEAiBMDABAnBgAgTgwAQJwYAIA4MQAAcWIAAOLEAADEiQEAiBMDABAnBgAgTgwAQJwYAIA4MQAAcWIAAOLEAADEiQEAiBMDABAnBgAgTgwAQJwYAIA4MQAAcWIAAOLEAADEiQEAiBMDABAnBgAgTgwAQJwYAIA4MQAAcWIAAOLEAADEiQEAiBMDABAnBgAgTgwAQJwYAIA4MQAAcWIAAOLEAADEiQEAiBMDABAnBgAgTgwAQJwYAIA4MQAAcWIAAOLEAADEiQEAiBMDABAnBgAgTgwAQJwYAIA4MQAAcWIAAOLEAADEiQEAiBMDABAnBgAgTgwAQJwYAIA4MQAAcWIAAOLEAADEiQEAiBMDABAnBgAgTgwAQJwYAIA4MQAAcWIAAOLEAADEiQEAiBMDABAnBgAgTgwAQJwYAIA4MQAAcWIAAOLEAADEiQEAiBMDABAnBgAgTgwAQJwYAIA4MQAAcWIAAOLEAADEiQEAiBMDABAnBgAgTgwAQJwYAIA4MQAAcWIAAOLEAADEiQEAiBMDABAnBgAgTgwAQJwYAIA4MQAAcWIAAOLEAADEiQEAiBMDABAnBgAgTgwAQJwYAIA4MQAAcWIAAOLEAADEiQEAiBMDABAnBgAgTgwAQNzcnlw3bgEXrOEoOYuT5K28DABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxYgAA4sQAAMTN/dF13xZwhZPkME6St/IyAABxYgAA4sQAAMSJAQCIEwMAECcGACBODABAnBgAgDgxAABxP3cxEbkocGJOAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.imshow(img_rgb)\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9fe2124e-5097-450a-a5b9-c5cf93c919ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "row = df_with_features_3d.select(\"features_3d\").limit(1).collect()[0]\n",
    "features_3d_list = row[\"features_3d\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "254c849f-e539-4638-8594-fa97111a3b53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img_rgb = np.array(features_3d_list, dtype=np.float32)\n",
    "img_rgb_uint8 = img_rgb.astype(np.uint8)\n",
    "\n",
    "plt.imshow(img_rgb)\n",
    "plt.axis(\"off\")  # Hide axis ticks\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "865de66c-b1a2-4366-a7f5-5631289fe321",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(img_rgb/255.0)\n",
    "plt.axis(\"off\")  # Hide axis ticks\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "41b866ed-ab0b-4b77-8058-7cd6cbed63f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "df_with_features_3d.drop(col(\"image\")).write.mode(\"overwrite\").parquet(\"test/test_df_3d\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f9feeb1c-0c62-4cc6-af49-21762732099a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated row size: 68421.57 KB\n"
     ]
    }
   ],
   "source": [
    "# Collect a single row\n",
    "row = df_with_features_3d.limit(1).collect()[0]\n",
    "\n",
    "# Convert the row data to its approximate size\n",
    "def estimate_row_size(row):\n",
    "    total_size = 0\n",
    "\n",
    "    for column_name, value in row.asDict().items():\n",
    "        if isinstance(value, list):\n",
    "            # Recursively estimate size of lists\n",
    "            total_size += estimate_list_size(value)\n",
    "        elif isinstance(value, (str, int, float, bytes)):\n",
    "            # Add size of primitive types\n",
    "            total_size += sys.getsizeof(value)\n",
    "        elif value is None:\n",
    "            # Null values have negligible size\n",
    "            continue\n",
    "        else:\n",
    "            # Handle other unexpected types\n",
    "            total_size += sys.getsizeof(value)\n",
    "\n",
    "    return total_size\n",
    "\n",
    "def estimate_list_size(lst):\n",
    "    \"\"\"Recursively calculate size of a nested list.\"\"\"\n",
    "    total_size = sys.getsizeof(lst)  # Base size of the list object\n",
    "    for item in lst:\n",
    "        if isinstance(item, list):\n",
    "            total_size += estimate_list_size(item)  # Recursively handle nested lists\n",
    "        elif isinstance(item, (str, int, float, bytes)):\n",
    "            total_size += sys.getsizeof(item)\n",
    "        elif isinstance(item, np.ndarray):\n",
    "            total_size += item.nbytes  # Size of NumPy arrays\n",
    "        else:\n",
    "            total_size += sys.getsizeof(item)\n",
    "    return total_size\n",
    "\n",
    "# Estimate size of the row\n",
    "row_size_in_bytes = estimate_row_size(row)\n",
    "\n",
    "print(f\"Estimated row size: {row_size_in_bytes / 1024:.2f} KB\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "edbd2dd8-6365-4348-9828-af0545afa719",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.sql.functions import input_file_name\n",
    "\n",
    "uuid_file_path_df = result_df.withColumn(\"file_path\", input_file_name()).select(\"uuid\", \"file_path\")\n",
    "uuid_file_path_df.show(5, truncate=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45bb4cb8-9f30-4a69-bf6e-192ad18fc072",
   "metadata": {},
   "source": [
    "# Save Images"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31a1bc19-de24-4a69-9a4c-3360a94a1c7a",
   "metadata": {},
   "source": [
    "This section demonstrates how to:\n",
    "\n",
    "- Collect a subset of image data from a Spark DataFrame.\n",
    "- Write batch images to disk, either sequentially or in parallel across multiple partitions.\n",
    "- Optimize processing by repartitioning data for distributed execution and handling large datasets efficiently.\n",
    "\n",
    "We use the following:\n",
    "\n",
    "`save_images_partition`: A function that processes a spark data partition and iteratively writes images to disk.\n",
    "\n",
    "`result_df`: A Spark DataFrame containing all images from museum specimens, with 9,490,931 rows.\n",
    "\n",
    "`sample_df`: A smaller Spark DataFrame subset of result_df, containing 947 rows (674.93 MB)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7a10e987-7d35-4f52-9a51-97bebcb40381",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import io\n",
    "import tarfile\n",
    "import shutil\n",
    "\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "from tqdm import tqdm\n",
    "\n",
    "from pyspark.sql import SparkSession\n",
    "from pyspark.sql import functions as F\n",
    "from pyspark.sql import types as T\n",
    "from pyspark.sql.functions import col\n",
    "\n",
    "spark= SparkSession.getActiveSession()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18973e08-8492-48dc-9f1f-8769ab203163",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "sample_df = spark.read.parquet(f\"{BASE_PATH}/gbif/image_lookup/multi_images_museum_specimen/group_id=0/part-00000-424c8415-fd13-41a6-8e35-6d99af3c9f79-c000.snappy.parquet\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab8a7a99-6850-4d58-942e-1acbae8ead35",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "result_df = spark.read.parquet(f\"{BASE_PATH}/gbif/image_lookup/multi_images_museum_specimen\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cc522082-24d1-4c52-b904-db4d80a47e7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def decode_image_to_pil(row_data):\n",
    "    \"\"\"\n",
    "    Decode binary image data from a DataFrame row and convert it to a PIL Image.\n",
    "\n",
    "    Parameters:\n",
    "    - row_data: pyspark.sql.Row\n",
    "        A row containing `image`, `original_size`, and `resized_size` fields.\n",
    "\n",
    "    Returns:\n",
    "    - pil_image: PIL.Image.Image\n",
    "        The decoded and converted PIL Image.\n",
    "    \"\"\"\n",
    "    image_bytes = row_data[\"image\"]\n",
    "\n",
    "    try:\n",
    "        # Try using the original size\n",
    "        original_height, original_width = row_data[\"original_size\"]\n",
    "        img_array = np.frombuffer(image_bytes, dtype=np.uint8).reshape((original_height, original_width, 3))\n",
    "    except Exception:\n",
    "        # Fallback to resized size if original size is unavailable or invalid\n",
    "        resized_height, resized_width = row_data[\"resized_size\"]\n",
    "        img_array = np.frombuffer(image_bytes, dtype=np.uint8).reshape((resized_height, resized_width, 3))\n",
    "\n",
    "    # Convert BGR to RGB\n",
    "    image = img_array[..., ::-1]\n",
    "\n",
    "    # Convert NumPy array to PIL Image\n",
    "    pil_image = Image.fromarray(image, mode=\"RGB\")\n",
    "\n",
    "    return pil_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5bee523d-0dbb-4e72-93e0-efc9b52bb1f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compress_dir(dir_path, tar_file_path, compress=True):\n",
    "    \"\"\"\n",
    "    Replace a folder with its TAR or TAR.GZ file.\n",
    "\n",
    "    Parameters:\n",
    "    - dir_path (str): Path to the folder to archive and delete.\n",
    "    - tar_file_path (str): Path for the resulting TAR file (without .tar/.tar.gz extension).\n",
    "    - compress (bool): Whether to compress the archive with Gzip (default True).\n",
    "    \"\"\"\n",
    "    mode = \"w:gz\" if compress else \"w\"  # Compression mode\n",
    "    extension = \".tar.gz\" if compress else \".tar\"\n",
    "    tar_file_path += extension  # Add appropriate extension\n",
    "\n",
    "    try:\n",
    "        # Step 1: Create the TAR archive\n",
    "        with tarfile.open(tar_file_path, mode) as tar:\n",
    "            tar.add(dir_path, arcname=os.path.basename(dir_path))\n",
    "        print(f\"Successfully created TAR file: {tar_file_path}\")\n",
    "\n",
    "        # Step 2: Verify the TAR file creation\n",
    "        if os.path.exists(tar_file_path):\n",
    "            # Step 3: Delete the original folder\n",
    "            shutil.rmtree(dir_path)\n",
    "            print(f\"Original folder '{dir_path}' deleted after archiving.\")\n",
    "        else:\n",
    "            print(f\"Error: TAR file not created. Folder '{dir_path}' was not deleted.\")\n",
    "    except Exception as e:\n",
    "        print(f\"Error during archiving or deletion: {e}\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d2639077-3de7-4515-b79f-0c69d9d3e47d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_images_partition(rows, output_dir, compress = False):\n",
    "    \"\"\"\n",
    "    Save images from a list of rows to the specified output directory.\n",
    "\n",
    "    Parameters:\n",
    "    - rows: iterable\n",
    "        List of pyspark.sql.Row objects containing `image`, `original_size`, `resized_size`, and `uuid` fields.\n",
    "    - output_dir: str\n",
    "        The directory where images will be saved.\n",
    "    \"\"\"\n",
    "    rows = list(rows)\n",
    "    print(f\"Processing {len(rows)} rows in partition by PID: {os.getpid()}\")\n",
    "\n",
    "    # Create the partition-specific directory\n",
    "    partition_output_dir = os.path.join(output_dir, f\"partition_{os.getpid()}\")\n",
    "    os.makedirs(partition_output_dir, exist_ok=True)\n",
    "    \n",
    "    # Wrap the rows in tqdm for progress tracking\n",
    "    for row in tqdm(rows, desc=f\"PID {os.getpid()} Progress\", unit=\"image\"):\n",
    "        try:\n",
    "            # Decode the image and get the UUID\n",
    "            pil_image = decode_image_to_pil(row)\n",
    "            uuid = row[\"uuid\"]\n",
    "            \n",
    "            # Define the output file path and save the image\n",
    "            output_path = os.path.join(partition_output_dir, f\"{uuid}.png\")\n",
    "            pil_image.save(output_path)\n",
    "            \n",
    "        except Exception as e:\n",
    "            print(f\"Error saving image for UUID {row['uuid']}: {e}\")\n",
    "    print(f\"Written {len(rows)} images to {partition_output_dir}\")\n",
    "\n",
    "    # Compresss folder into tar files\n",
    "    if compress:\n",
    "        print(f\"Compressing...\")        \n",
    "        compress_dir(\n",
    "            dir_path = partition_output_dir,\n",
    "            tar_file_path = output_dir,\n",
    "            compress = True\n",
    "        )\n",
    "        print(f\"Images compressed into {output_dir}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f0ef568-15ff-4feb-94f3-747844589bd4",
   "metadata": {},
   "source": [
    "## Writing Images Sequentially from a Single Partition"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba2559e2-dc82-4202-9ecd-fd3a9f92a86f",
   "metadata": {},
   "source": [
    "In this step:\n",
    "\n",
    "1. We collect 300 rows from `sample_df` into the **driver node** as a **single partition**.\n",
    "2. Use `save_images_partition` to write all the images to disk. Since the data is collected to the driver node, progress is visible in the Jupyter session.\n",
    "\n",
    "Since we are on the drivers node we can see the console progress output."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d20fa7d-a12f-4450-9921-9131622721c8",
   "metadata": {},
   "source": [
    "**Code Example: Writing Without Compression**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "81643667-935b-442c-babb-ff60655f60ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "output_dir = f\"{BASE_PATH}/gbif/image_lookup/test/single_partition\"\n",
    "# Remove output directory generated from previous executions\n",
    "shutil.rmtree(output_dir) if os.path.exists(output_dir) else None \n",
    "\n",
    "\n",
    "save_images_partition(sample_df.limit(300).collect(), output_dir, compress=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9940f51f-ca3c-4d1d-8323-45d440a49b43",
   "metadata": {},
   "source": [
    "**Code Example: Writing With Compression**\n",
    "\n",
    "To address file count restrictions on the OSC storage system, compress the images into a single TAR file. \n",
    "\n",
    "The compressed TAR file can be downloaded and viewed locally in a file explorer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce1ee48a-833c-4192-afe5-24cd00a0fcd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "shutil.rmtree(output_dir) if os.path.exists(output_dir) else None\n",
    "save_images_partition(sample_df.limit(300).collect(), output_dir, compress=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f3546b5-546b-4217-abc4-d9452fdda198",
   "metadata": {},
   "source": [
    "## Writing Images in Parallel Across Multiple Partitions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0ec6666-c4a9-4fe9-b41a-f2bbc7601ed7",
   "metadata": {},
   "source": [
    "Instead of collecting all data into the driver node, which is computationally expensive, we distribute the processing across worker nodes by:\n",
    "\n",
    "1. Repartitioning data into your workers/executors.\n",
    "2. Writing images for each partition in parallel."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97e02175-32a6-4408-b2bc-685517e86354",
   "metadata": {},
   "source": [
    "Here, we limit the dataset to 900 rows and repartition it into 3 partitions, ensuring each partition contains 300 images."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74348b29-826a-4700-a502-bd4ca37eb3bf",
   "metadata": {},
   "source": [
    "**Code Example: Writing Without Compression**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d64ef6cc-ca44-4f82-ae58-9561544b4f67",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "output_dir = f\"{BASE_PATH}/gbif/image_lookup/test/multiple_partitions\"\n",
    "shutil.rmtree(output_dir) if os.path.exists(output_dir) else None\n",
    "\n",
    "sample_df.limit(900).repartition(3).foreachPartition(lambda partition: save_images_partition(partition, output_dir, compress=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f25a431b-f225-4947-a9b0-b6d33151643e",
   "metadata": {},
   "source": [
    "**Code Example: Writing With Compression**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31e2f788-be96-473e-a3f4-5d606e5b290f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "output_dir = f\"{BASE_PATH}/gbif/image_lookup/test/multiple_partitions\"\n",
    "shutil.rmtree(output_dir) if os.path.exists(output_dir) else None\n",
    "\n",
    "sample_df.limit(900).repartition(3).foreachPartition(lambda partition: save_images_partition(partition, output_dir, compress=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20be7b9f-2a71-4bdd-b644-044b53bdd04c",
   "metadata": {},
   "source": [
    "## Filtering Subsets and Write in Parallel"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35e44c74-48b7-492f-93c8-dc3bb8cbf1de",
   "metadata": {},
   "source": [
    "You can also create subset from the data using Spark's DataFrame API or SQL interface. \n",
    "\n",
    "Check the row count, and then repartition the data into moderate sizes, and then write to disk in parallel. \n",
    "\n",
    "For example, filter rows where `basisOfRecord` is `MATERIAL_CITATION`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "84acdef7-bef7-4200-a367-70ffdee780b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1395"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subset_df = (\n",
    "    result_df\n",
    "    .filter(col(\"basisOfRecord\") == \"MATERIAL_CITATION\")\n",
    ")\n",
    "subset_df.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cacf50b6-70c1-438a-88a7-baebc0611838",
   "metadata": {},
   "source": [
    "Each workers are allocated with limited memory during the Spark initlization, therefore we want to limit the partition size to avoid out-of-memory error. My current spark session workers could easily handle 300 images writing tasks. \n",
    "\n",
    "So I'll repartition this subset into 5 partitions such that each workers will write approximately 220 images in one tar file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b7c4d8c7-07ae-4d38-87f2-9781f0482aa6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Stage 34:==================================>                       (3 + 2) / 5]\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 137 ms, sys: 29 ms, total: 166 ms\n",
      "Wall time: 1min 25s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "output_dir = f\"{BASE_PATH}/gbif/image_lookup/test/materal_citation\"\n",
    "shutil.rmtree(output_dir) if os.path.exists(output_dir) else None\n",
    "\n",
    "subset_df.repartition(5).foreachPartition(lambda partition: save_images_partition(partition, output_dir, compress = True))"
   ]
  }
 ],
 "metadata": {
  "jupytext": {
   "formats": "ipynb,py:percent"
  },
  "kernelspec": {
   "display_name": "PySpark",
   "language": "python",
   "name": "pyspark"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
